| 1. |
- Hess, Georg, et al.
(författare)
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Real-world experience among patients with relapsed/refractory mantle cell lymphoma after Bruton tyrosine kinase inhibitor failure in Europe : The SCHOLAR-2 retrospective chart review study
- 2023
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Ingår i: British Journal of Haematology. - : Wiley. - 0007-1048 .- 1365-2141. ; 202:4, s. 749-759
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Tidskriftsartikel (refereegranskat)abstract
- Mantle cell lymphoma (MCL) after relapse is associated with poor prognosis. No standard of care exists and available evidence for treatments is limited, particularly in patients who fail Bruton tyrosine kinase inhibitor (BTKi) therapy. This multicentre retrospective chart review study, SCHOLAR-2, addresses this knowledge gap and reports on data collected from 240 patients with relapsed/refractory MCL in Europe who were treated with BTKi-based therapy between July 2012 and July 2018, and had experienced disease progression while on BTKi therapy or discontinued BTKi therapy due to intolerance. The median overall survival (OS) from initiation of first BTKi therapy was 14.6 months (95% confidence interval [CI] 11.6–20.0) in the overall cohort, 5.5 months (95% CI 3.9–8.2) in 91 patients without post-BTKi therapy, and 23.8 months (95% CI 18.9–30.1) in 149 patients who received post-BTKi therapy (excluding chimeric antigen receptor T-cell treatment). In the latter group, patients received a median of one (range, one to seven) line of post-BTKi therapy, with lenalidomide-containing regimens and bendamustine plus rituximab being the most frequently administered; the median OS from initiation of first post-BTKi therapy was 9.7 months (95% CI 6.3–12.7). These results provide a benchmark for survival in patients with R/R MCL receiving salvage therapy after BTKi failure.
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| 2. |
- Häggström, Ida, 1982, et al.
(författare)
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Deep learning for [ 18 F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
- 2024
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Ingår i: The Lancet Digital Health. - 2589-7500. ; 6:2, s. e114-e125
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Tidskriftsartikel (refereegranskat)abstract
- Background : The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. Methods : In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1–3 vs 4–5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. Findings : In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942–0·956), accuracy of 0·890 (0·879–0·901), sensitivity of 0·868 (0·851–0·885), and specificity of 0·913 (0·899–0·925); LARS-max achieved an AUC of 0·949 (0·942–0·956), accuracy of 0·868 (0·858–0·879), sensitivity of 0·909 (0·896–0·924), and specificity of 0·826 (0·808–0·843); and LARS-ptct achieved an AUC of 0·939 (0·930–0·948), accuracy of 0·875 (0·864–0·887), sensitivity of 0·836 (0·817–0·855), and specificity of 0·915 (0·901–0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938–0·966), accuracy of 0·907 (0·888–0·925), sensitivity of 0·874 (0·843–0·904), and specificity of 0·949 (0·921–0·960); LARS-max achieved an AUC of 0·952 (0·937–0·965), accuracy of 0·898 (0·878–0·916), sensitivity of 0·899 (0·871–0·926), and specificity of 0·897 (0·871–0·922); and LARS-ptct achieved an AUC of 0·932 (0·915–0·948), accuracy of 0·870 (0·850–0·891), sensitivity of 0·827 (0·793–0·863), and specificity of 0·913 (0·889–0·937). Interpretation : Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Funding: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.
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| 3. |
- Moore, Amy, et al.
(författare)
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Genetically Determined Height and Risk of Non-hodgkin Lymphoma
- 2020
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Ingår i: Frontiers in Oncology. - : FRONTIERS MEDIA SA. - 2234-943X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00-1.17, p = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01-1.31, p = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes.
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